Systems Architect AI/ML Infrastructure

Define and drive the end-to-end infrastructure architecture for Deepgram's AI/ML workloads across production inference and research training

Design multi-cloud and hybrid infrastructure strategies that balance performance, reliability, cost, and vendor flexibility

Architect compute orchestration systems that efficiently schedule and manage GPU and CPU workloads across heterogeneous infrastructure

Design storage architectures that handle the massive datasets required for speech and audio ML -- from high-throughput training data pipelines to low-latency model serving

Lead capacity planning across all infrastructure dimensions, modeling growth and ensuring Deepgram can scale ahead of demand

Drive cost optimization and FinOps practices, identifying opportunities to reduce infrastructure spend without compromising performance or reliability

Design burstable, elastic training infrastructure that can scale up for large training runs and scale down to minimize idle cost

Architect research compute infrastructure that gives ML teams the resources they need while maintaining operational efficiency

Establish architectural standards, design review processes, and technical documentation practices for infrastructure decisions

Collaborate with engineering leadership to align infrastructure strategy with product roadmap and business objectives

Evaluate emerging hardware, cloud services, and infrastructure technologies for potential adoption

Think in systems -- you naturally see the connections between compute, storage, network, and how they interact under load

Are motivated by designing infrastructure that operates at the intersection of real-time production systems and large-scale ML training

Enjoy making architectural trade-offs where cost, performance, reliability, and velocity are all in tension

Want to work across the full infrastructure stack -- from bare metal and GPUs to cloud services and container orchestration

Are excited about building cost-effective, burstable infrastructure that enables world-class AI research

Like operating at a strategic level while staying technically deep enough to validate designs and debug complex issues

7+ years of experience in infrastructure engineering, systems architecture, or a senior technical role focused on large-scale infrastructure

Proven experience designing multi-cloud architectures spanning AWS and at least one other major cloud provider or on-premises environment

Deep expertise in storage system design -- block, object, and file storage, including performance tuning for large-scale data workloads

Strong experience with compute orchestration using Kubernetes, and an understanding of how to schedule diverse workloads efficiently

Hands-on experience with GPU infrastructure -- procurement considerations, cluster design, driver and runtime management

Track record of capacity planning and infrastructure scaling for high-growth environments

Ability to communicate complex architectural decisions clearly to both technical and non-technical stakeholders

Strong understanding of networking fundamentals as they relate to infrastructure architecture (see our Network Engineer role for the deep specialist)

Direct experience architecting infrastructure for ML training workloads -- distributed training, large dataset management, experiment infrastructure

Background in cost optimization and FinOps practices for large-scale cloud and bare metal infrastructure

Experience operating and managing bare metal infrastructure in colocation facilities

Expertise in network architecture design, including high-bandwidth GPU interconnects and global traffic routing

Experience with infrastructure modeling and simulation for capacity planning

Familiarity with Slurm, Ray, or other HPC/ML job scheduling systems

Understanding of power, cooling, and physical infrastructure considerations for GPU-dense deployments

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